Spatio-Temporal Reconstruction and Visualization of Plant Growth for Phenotyping

Jacket Demby's, Ali Shafiekhani, Felix B. Fritschi, Guilherme N. DeSouza

Abstract: Recently, there has been increasing interest in applying spatio-temporal registration for phenotyping of both individual and groups of plants in large agricultural fields. However, 3D non-rigid methods for registration are still a research topic and present numerous particular challenges in plant phenotyping due to: overlaps and self-occlusions in dense phyllotaxies; deformations caused by plant growth over time; changes in outdoor environmental settings, etc. In this paper, we address the problem of registering spatio-temporal 3D models of plants by proposing a bundle registration approach that can handle transformations with up to three additional Degrees of Freedom (DoF) to capture the growth of the plant. Besides, we offer to the research community a new multi-view stereo dataset consisting of 2D images and 3D point clouds of an African violet plant observed over a period of ten days. We evaluate the proposed algorithm on the new African violet dataset using the usual 6 DoF (three rotations and three translations) and compared it with 7 DoF (three rotations, three translations, and one scale) and 9 (three rotations, three translations, and three scales). We also performed the comparison between the proposed approach and two other registration approaches: pairwise and incremental. We show that the proposed algorithm achieves an average registration error of less than 2 mm on the African violet dataset. Also, we used VisND, an N-dimensional spatio-temporal visualization tool, to perform a visual assessment of the aligned time-varying 3D models of the plants.